Data Architect

We never ask for any kind of payment as part of our selection process, and we always contact candidates via our corporate accounts and platforms. If you are approached for payment or asked to make a purchase, this is likely to be fraudulent. Please check to see whether the role you are interested in is posted here, on our website. TMF Group is a leading provider of administrative services, helping clients invest and operate safely around the world. As we’re a global company with 11,000+ colleagues based in over 125 offices across 87 jurisdictions, we actively seek out people with the talent and potential to flourish at TMF Group, whatever their background, and offer job opportunities to the broadest spectrum of people. Once on board we nurture and promote talented individuals, making sure that senior positions are open to all. TMF Group is looking for an experienced Data Architect to join its Data Intelligence practice. In this role you will define how data is structured, integrated, governed and delivered across the organization. You will set the architectural direction for our enterprise data platform and turn business needs into scalable, secure and well‑governed data solutions. You will work closely with solution leads, data engineers, BI developers, product owners and client stakeholders. You will own the target‑state architecture, the data models and the standards that engineering teams build to, and you will be hands‑on enough to prototype, review and troubleshoot. This role has immediate impact: it shapes how financial and operational data from across our business lines comes together to support reporting, advanced analytics and AI.

Key Responsibilities
- Architecture Strategy and Standards
- Define, document and maintain the target‑state data architecture for the Data Intelligence practice, covering ingestion, storage (lake, lakehouse and warehouse), integration, semantic and consumption layers, with a roadmap tied to business priorities.
- Set architecture principles, standards and reusable reference patterns (e.g., medallion layering, change data capture, API‑based integration, semantic modelling) that engineering teams apply consistently.
- Evaluate emerging data and AI technologies, lead proofs of concept and make evidence‑based build, buy and platform recommendations.
- Solution Architecture and Design
- Translate business and client requirements into end‑to‑end solution architectures that meet functional needs and non‑functional requirements for scalability, performance, availability, security and cost.
- Produce and maintain architecture artefacts, including solution designs, data flow and lineage diagrams, integration specifications and architecture decision records.
- Lead or take part in design and architecture reviews to make sure solutions follow agreed standards before build and release.
- Own conceptual, logical and physical data models across enterprise and domain areas, including dimensional models, normalized models and data vault where appropriate.
- Design canonical models for core business entities such as clients, legal entities, funds, investors, accounts and transactions across financial and operational source systems.
- Design semantic layers and certified datasets that give BI and self‑service analytics users consistent, trusted definitions.
- Data Integration and Platform
- Define integration patterns (batch, CDC, event and streaming, REST APIs) for moving data between business applications, databases and the enterprise data platform.
- Guide platform design on Microsoft Azure, Microsoft Fabric, Databricks and Snowflake, including storage and partitioning strategy, performance tuning, workload management and cost optimization.
- Work with data engineers on the design of ingestion and transformation pipelines (ETL/ELT), reviewing their approach and building prototypes of complex components when needed.
- Data Governance, Quality and Security
- Work with data governance and business data owners to put ownership, stewardship, metadata management, cataloguing and lineage into practice.
- Define the data quality framework, covering profiling standards, data quality rules, monitoring, thresholds and remediation workflows, and see that it is built into delivery pipelines.
- Design master and reference data management for key entities to provide a single, reconciled view across systems.
- Build security and privacy into the architecture through data classification, role‑based access, masking, encryption and retention, in line with GDPR, other applicable regulations and client contractual obligations.
- Analytics and AI Enablement
- Design data foundations that support advanced analytics, machine learning and generative AI use cases, including feature‑ready data, governed access and integration with MLOps practices.
- Work with analytics and data science teams so that models and AI solutions are built on trusted, well‑documented data.
- Leadership and Co